Metal Doping Regulates Electrocatalysts Restructuring during Oxygen Evolution Reaction.

High-efficiency and low-cost catalysts for oxygen evolution reaction (OER) are critical for electrochemical water splitting to generate hydrogen, which is a clean fuel for sustainable energy conversion and storage. Among the emerging OER catalysts, transition metal dichalcogenides have exhibited superior activity compared to commercial standards such as RuO2, but inferior stability due to uncontrolled restructuring with OER. In this study, we create bimetallic sulfide catalysts by adapting the atomic ratio of Ni and Co in CoxNi1-xSy electrocatalysts to investigate the intricate restructuring processes. Surface-sensitive X-ray photoelectron spectroscopy and bulk-sensitive X-ray absorption spectroscopy confirmed the favorable restructuring of transition metal sulfide material following OER processes. Our results indicate that a small amount of Ni substitution can reshape the Co local electronic structure, which regulates the restructuring process to optimize the balance between OER activity and stability. This work represents a significant advancement in the development of efficient and noble metal-free OER electrocatalysts through a doping-regulated restructuring approach.

Effect of Phosphorus Modulation in Iron Single-Atom Catalysts for Peroxidase Mimicking.

Fe-N-C single-atom catalysts (SACs) exhibit excellent peroxidase (POD)-like catalytic activity, owing to their well-defined isolated iron active sites on the carbon substrate, which effectively mimic the structure of natural peroxidase's active center. To further meet the requirements of diverse biosensing applications, SAC POD-like activity still needs to be continuously enhanced. Herein, a phosphorus (P) heteroatom is introduced to boost the POD-like activity of Fe-N-C SACs. A 1D carbon nanowire (FeNCP/NW) catalyst with enriched Fe-N4 active sites is designed and synthesized, and P atoms are doped in the carbon matrix to affect the Fe center through long-range interaction. The experimental results show that the P-doping process can boost the POD-like activity more than the non-P-doped one, with excellent selectivity and stability. The mechanism analysis results show that the introduction of P into SAC can greatly enhance POD-like activity initially, but its effect becomes insignificant with increasing amount of P. As a proof of concept, FeNCP/NW is employed in an enzyme cascade platform for highly sensitive colorimetric detection of the neurotransmitter acetylcholine.

NiFe Nanoparticle Nest Supported on Graphene as Electrocatalyst for Highly Efficient Oxygen Evolution Reaction.

Designing cost-efffective electrocatalysts for the oxygen evolution reaction (OER) holds significant importance in the progression of clean energy generation and efficient energy storage technologies, such as water splitting and rechargeable metal-air batteries. In this work, an OER electrocatalyst is developed using Ni and Fe precursors in combination with different proportions of graphene oxide. The catalyst synthesis involved a rapid reduction process, facilitated by adding sodium borohydride, which successfully formed NiFe nanoparticle nests on graphene support (NiFe NNG). The incorporation of graphene support enhances the catalytic activity, electron transferability, and electrical conductivity of the NiFe-based catalyst. The NiFe NNG catalyst exhibits outstanding performance, characterized by a low overpotential of 292.3 mV and a Tafel slope of 48 mV dec-1, achieved at a current density of 10 mA cm- 2. Moreover, the catalyst exhibits remarkable stability over extended durations. The OER performance of NiFe NNG is on par with that of commercial IrO2 in alkaline media. Such superb OER catalytic performance can be attributed to the synergistic effect between the NiFe nanoparticle nests and graphene, which arises from their large surface area and outstanding intrinsic catalytic activity. The excellent electrochemical properties of NiFe NNG hold great promise for further applications in energy storage and conversion devices.

Active Sites of Cobalt Phthalocyanine in Electrocatalytic CO2 Reduction to Methanol.

Many metal coordination compounds catalyze CO2 electroreduction to CO, but cobalt phthalocyanine hybridized with conductive carbon such as carbon nanotubes is currently the only one that can generate methanol. The underlying structure-reactivity correlation and reaction mechanism desperately demand elucidation. Here we report the first in situ X-ray absorption spectroscopy characterization, combined with ex situ spectroscopic and electrocatalytic measurements, to study CoPc-catalyzed CO2 reduction to methanol. Molecular dispersion of CoPc on CNT surfaces, as evidenced by the observed electronic interaction between the two, is crucial to fast electron transfer to the active sites and multi-electron CO2 reduction. CO, the key intermediate in the CO2 -to-methanol pathway, is found to be labile on the active site, which necessitates a high local concentration in the microenvironment to compete with CO2 for active sites and promote methanol production. A comparison of the electrocatalytic performance of structurally related porphyrins indicates that the bridging aza-N atoms of the Pc macrocycle are critical components of the CoPc active site that produces methanol. In situ X-ray absorption spectroscopy identifies the active site as Co(I) and supports an increasingly non-centrosymmetric Co coordination environment at negative applied potential, likely due to the formation of a Co-CO adduct during the catalysis.

Reversible Cl/Cl- redox in a spinel Mn3O4 electrode.

A unique prospect of using halides as charge carriers is the possibility of the halides undergoing anodic redox behaviors when serving as charge carriers for the charge-neutrality compensation of electrodes. However, the anodic conversion of halides to neutral halogen species has often been irreversible at room temperature due to the emergence of diatomic halogen gaseous products. Here, we report that chloride ions can be reversibly converted to near-neutral atomic chlorine species in the Mn3O4 electrode at room temperature in a highly concentrated chloride-based aqueous electrolyte. Notably, the Zn2+ cations inserted in the first discharge and trapped in the Mn3O4 structure create an environment to stabilize the converted chlorine atoms within the structure. Characterization results suggest that the Cl/Cl- redox is responsible for the observed large capacity, as the oxidation state of Mn barely changes upon charging. Computation results corroborate that the converted chlorine species exist as polychloride monoanions, e.g., [Cl3]- and [Cl5]-, inside the Zn2+-trapped Mn3O4, and the presence of polychloride species is confirmed experimentally. Our results point to the halogen plating inside electrode lattices as a new charge-storage mechanism.

Regulating Catalytic Properties and Thermal Stability of Pt and PtCo Intermetallic Fuel-Cell Catalysts via Strong Coupling Effects between Single-Metal Site-Rich Carbon and Pt.

Developing low platinum-group-metal (PGM) catalysts for the oxygen reduction reaction (ORR) in proton-exchange membrane fuel cells (PEMFCs) for heavy-duty vehicles (HDVs) remains a great challenge due to the highly demanded power density and long-term durability. This work explores the possible synergistic effect between single Mn site-rich carbon (MnSA-NC) and Pt nanoparticles, aiming to improve intrinsic activity and stability of PGM catalysts. Density functional theory (DFT) calculations predicted a strong coupling effect between Pt and MnN4 sites in the carbon support, strengthening their interactions to immobilize Pt nanoparticles during the ORR. The adjacent MnN4 sites weaken oxygen adsorption at Pt to enhance intrinsic activity. Well-dispersed Pt (2.1 nm) and ordered L12-Pt3Co nanoparticles (3.3 nm) were retained on the MnSA-NC support after indispensable high-temperature annealing up to 800 °C, suggesting enhanced thermal stability. Both PGM catalysts were thoroughly studied in membrane electrode assemblies (MEAs), showing compelling performance and durability. The Pt@MnSA-NC catalyst achieved a mass activity (MA) of 0.63 A mgPt-1 at 0.9 ViR-free and maintained 78% of its initial performance after a 30,000-cycle accelerated stress test (AST). The L12-Pt3Co@MnSA-NC catalyst accomplished a much higher MA of 0.91 A mgPt-1 and a current density of 1.63 A cm-2 at 0.7 V under traditional light-duty vehicle (LDV) H2-air conditions (150 kPaabs and 0.10 mgPt cm-2). Furthermore, the same catalyst in an HDV MEA (250 kPaabs and 0.20 mgPt cm-2) delivered 1.75 A cm-2 at 0.7 V, only losing 18% performance after 90,000 cycles of the AST, demonstrating great potential to meet the DOE targets.

Ball Milling-Enabled Fe2.4+ to Fe3+ Redox Reaction in Prussian Blue Materials for Long-Life Aqueous Sodium-Ion Batteries.

Aqueous Na-ion batteries using Prussian blue materials have inherent advantages in safety, material sustainability, and economic cost. However, it is challenging to obtain long-term cycling stability because many redox reactions have poor intrinsic stability in water. Here, we demonstrate reversible Fe2.4+ to Fe3+ redox reaction of Prussian blue electrodes cycled in a 17 m NaClO4 water-in-salt electrolyte. The cubic phase c-Na1.17Fe[Fe(CN)6]·0.35H2O) derived from monoclinic Prussian blue (m-Na1.88Fe[Fe(CN)6]·0.7H2O) through ball milling delivers excellent cycling stability of >18,000 cycles with >90% capacity retention at the 10C rate. The specific capacity is ∼75 and ∼67 mAh/g at 1C and 10C rates, respectively. Systematic characterizations including electron microscopy, X-ray diffraction, Fourier-transform infrared spectroscopy, X-ray photoelectron spectroscopy, and X-ray absorption spectroscopy have verified the phase transition and iron oxidation state evolution, revealing the mechanism that enables the material's high rate and long durability as the battery cathode.

Diagnostic Performance of Machine Learning-Derived Radiomics Signature of Pericoronary Adipose Tissue in Coronary Computed Tomography Angiography for Coronary Artery In-Stent Restenosis.

RATIONALE AND OBJECTIVES: Coronary inflammation can alter the perivascular fat phenotype. Hence, we aimed to assess the diagnostic performance of radiomics features of pericoronary adipose tissue (PCAT) in coronary computed tomography angiography (CCTA) for in-stent restenosis (ISR) after percutaneous coronary intervention. MATERIALS AND METHODS: In this study, 165 patients with 214 eligible vessels were included, and ISR was found in 79 vessels. After evaluating clinical and stent characteristics, peri-stent fat attenuation index, and PCAT volume, 1688 radiomics features were extracted from each peri-stent PCAT segmentation. The eligible vessels were randomly categorized into training and validation groups in a ratio of 7:3. After performing feature selection using Pearson's correlation, F test, and least absolute shrinkage and selection operator analysis, radiomics models and integrated models that combined selected clinical features and Radscore were established using five different machine learning algorithms (logistic regression, support vector machine, random forest, stochastic gradient descent, and XGBoost). Subgroup analysis was performed using the same method for patients with stent diameters of ≤ 3 mm. RESULTS: Nine significant radiomics features were selected, and the areas under the curves (AUCs) for the radiomics model and the integrated model were 0.69 and 0.79, respectively, for the validation group. The AUCs of the subgroup radiomics model based on 15 selected radiomics features and the subgroup integrated model were 0.82 and 0.85, respectively, for the validation group, which showed better diagnostic performance. CONCLUSION: CCTA-based radiomics signature of PCAT has the potential to identify coronary artery ISR without additional costs or radiation exposure.

Precursor-mediated in situ growth of hierarchical N-doped graphene nanofibers confining nickel single atoms for CO2 electroreduction.

Despite the various strategies for achieving metal-nitrogen-carbon (M-N-C) single-atom catalysts (SACs) with different microenvironments for electrochemical carbon dioxide reduction reaction (CO2RR), the synthesis-structure-performance correlation remains elusive due to the lack of well-controlled synthetic approaches. Here, we employed Ni nanoparticles as starting materials for the direct synthesis of nickel (Ni) SACs in one spot through harvesting the interaction between metallic Ni and N atoms in the precursor during the chemical vapor deposition growth of hierarchical N-doped graphene fibers. By combining with first-principle calculations, we found that the Ni-N configuration is closely correlated to the N contents in the precursor, in which the acetonitrile with a high N/C ratio favors the formation of Ni-N3, while the pyridine with a low N/C ratio is more likely to promote the evolution of Ni-N2. Moreover, we revealed that the presence of N favors the formation of H-terminated edge of sp2 carbon and consequently leads to the formation of graphene fibers consisting of vertically stacked graphene flakes, instead of the traditional growth of carbon nanotubes on Ni nanoparticles. With a high capability in balancing the *COOH formation and *CO desorption, the as-prepared hierarchical N-doped graphene nanofibers with Ni-N3 sites exhibit a superior CO2RR performance compared to that with Ni-N2 and Ni-N4 ones.

Atomically Local Electric Field Induced Interface Water Reorientation for Alkaline Hydrogen Evolution Reaction.

The slow water dissociation process in alkaline electrolyte severely limits the kinetics of HER. The orientation of H2 O is well known to affect the dissociation process, but H2 O orientation is hard to control because of its random distribution. Herein, an atomically asymmetric local electric field was designed by IrRu dizygotic single-atom sites (IrRu DSACs) to tune the H2 O adsorption configuration and orientation, thus optimizing its dissociation process. The electric field intensity of IrRu DSACs is over 4.00×1010  N/C. The ab initio molecular dynamics simulations combined with in situ Raman spectroscopy analysis on the adsorption behavior of H2 O show that the M-H bond length (M=active site) is shortened at the interface due to the strong local electric field gradient and the optimized water orientation promotes the dissociation process of interfacial water. This work provides a new way to explore the role of single atomic sites in alkaline hydrogen evolution reaction.

Three-dimensional Zn-based alloys for dendrite-free aqueous Zn battery in dual-cation electrolytes.

Aqueous zinc-ion batteries, in terms of integration with high safety, environmental benignity, and low cost, have attracted much attention for powering electronic devices and storage systems. However, the interface instability issues at the Zn anode caused by detrimental side reactions such as dendrite growth, hydrogen evolution, and metal corrosion at the solid (anode)/liquid (electrolyte) interface impede their practical applications in the fields requiring long-term performance persistence. Despite the rapid progress in suppressing the side reactions at the materials interface, the mechanism of ion storage and dendrite formation in practical aqueous zinc-ion batteries with dual-cation aqueous electrolytes is still unclear. Herein, we design an interface material consisting of forest-like three-dimensional zinc-copper alloy with engineered surfaces to explore the Zn plating/stripping mode in dual-cation electrolytes. The three-dimensional nanostructured surface of zinc-copper alloy is demonstrated to be in favor of effectively regulating the reaction kinetics of Zn plating/stripping processes. The developed interface materials suppress the dendrite growth on the anode surface towards high-performance persistent aqueous zinc-ion batteries in the aqueous electrolytes containing single and dual cations. This work remarkably enhances the fundamental understanding of dual-cation intercalation chemistry in aqueous electrochemical systems and provides a guide for exploring high-performance aqueous zinc-ion batteries and beyond.

Identification of a key ceRNA network associated with ferroptosis in gastric cancer.

Ferroptosis, a newly discovered irondependent form of regulated cell death caused by excessive accumulation of lipid peroxides, is linked to the development and treatment response of various types of cancer, including gastric cancer (GC). Noncoding RNAs (ncRNAs), as key regulators in cancer, have both oncogenic and tumor suppressive roles. However, studies on ferroptosis-related ncRNA networks in GC are still lacking. Here, we first identified 61 differentially expressed genes associated with ferroptosis in GC by computing and analyzing gene expression profile of tumor and normal tissues for GC. Then, upstream lncRNAs and miRNAs interacting with them were found through miRNet and miRBase databases, and hub lncRNAs and miRNAs were obtained through topological analysis. Finally, the ceRNA regulatory network linked to ferroptosis in GC was established, which includes two ferroptosis marker genes (TXNIP and TSC22D3), one driver gene (GABARAPL1), and one suppressor gene (CAV1). Kaplan-Meier survival analysis showed that changes in the expression of these genes were associated with the survival of GC patients. Furthermore, our study revealed that this ceRNA network may influence the progression of GC by regulating ferroptosis process. These results will help experimental researchers to design an experiment study to further explore the roles of this regulatory network in GC ferroptosis.

Carbon-Nitride-Based Materials for Advanced Lithium-Sulfur Batteries.

Lithium-sulfur (Li-S) batteries are promising candidates for next-generation energy storage systems owing to their high energy density and low cost. However, critical challenges including severe shuttling of lithium polysulfides (LiPSs) and sluggish redox kinetics limit the practical application of Li-S batteries. Carbon nitrides (CxNy), represented by graphitic carbon nitride (g-C3N4), provide new opportunities for overcoming these challenges. With a graphene-like structure and high pyridinic-N content, g-C3N4 can effectively immobilize LiPSs and enhance the redox kinetics of S species. In addition, its structure and properties including electronic conductivity and catalytic activity can be regulated by simple methods that facilitate its application in Li-S batteries. Here, the recent progress of applying CxNy-based materials including the optimized g-C3N4, g-C3N4-based composites, and other novel CxNy materials is systematically reviewed in Li-S batteries, with a focus on the structure-activity relationship. The limitations of existing CxNy-based materials are identified, and the perspectives on the rational design of advanced CxNy-based materials are provided for high-performance Li-S batteries.

Enhanced oxygen evolution over dual corner-shared cobalt tetrahedra.

Developing efficient catalysts is of paramount importance to oxygen evolution, a sluggish anodic reaction that provides essential electrons and protons for various electrochemical processes, such as hydrogen generation. Here, we report that the oxygen evolution reaction (OER) can be efficiently catalyzed by cobalt tetrahedra, which are stabilized over the surface of a Swedenborgite-type YBCo4O7 material. We reveal that the surface of YBaCo4O7 possesses strong resilience towards structural amorphization during OER, which originates from its distinctive structural evolution toward electrochemical oxidation. The bulk of YBaCo4O7 composes of corner-sharing only CoO4 tetrahedra, which can flexibly alter their positions to accommodate the insertion of interstitial oxygen ions and mediate the stress during the electrochemical oxidation. The density functional theory calculations demonstrate that the OER is efficiently catalyzed by a binuclear active site of dual corner-shared cobalt tetrahedra, which have a coordination number switching between 3 and 4 during the reaction. We expect that the reported active structural motif of dual corner-shared cobalt tetrahedra in this study could enable further development of compounds for catalyzing the OER.

Single-Atomic Iron Doped Carbon Dots with Both Photoluminescence and Oxidase-Like Activity.

Multifunctional nanozymes can benefit biochemical analysis via expanding sensing modes and enhancing analytical performance, but designing multifunctional nanozymes to realize the desired sensing of targets is challenging. In this work, single-atomic iron doped carbon dots (SA Fe-CDs) are designed and synthesized via a facile in situ pyrolysis process. The small-sized CDs not only maintain their tunable fluorescence, but also serve as a support for loading dispersed active sites. Monoatomic Fe offers SA Fe-CDs exceptional oxidase-mimetic activity to catalyze 3,3',5,5'-tetramethylbenzidine (TMB) oxidation with fast response (Vmax  = 10.4 nM s-1 ) and strong affinity (Km  = 168 µM). Meanwhile, their photoluminescence is quenched by the oxidation product of TMB due to inner filter effect. Phosphate ions (Pi) can suppress the oxidase-mimicking activity and restore the photoluminescence of SA Fe-CDs by interacting with Fe active sites. Based on this principle, a dual-mode colorimetric and fluorescence assay of Pi with high sensitivity, selectivity, and rapid response is established. This work paves a path to develop multifunctional enzyme-like catalysts, and offers a simple but efficient dual-mode method for phosphate monitoring, which will inspire the exploration of multi-mode sensing strategies based on nanozyme catalysis.

Prediction of metal ion ligand binding residues by adding disorder value and propensity factors based on deep learning algorithm.

Proteins need to interact with different ligands to perform their functions. Among the ligands, the metal ion is a major ligand. At present, the prediction of protein metal ion ligand binding residues is a challenge. In this study, we selected Zn2+, Cu2+, Fe2+, Fe3+, Co2+, Mn2+, Ca2+ and Mg2+ metal ion ligands from the BioLip database as the research objects. Based on the amino acids, the physicochemical properties and predicted structural information, we introduced the disorder value as the feature parameter. In addition, based on the component information, position weight matrix and information entropy, we introduced the propensity factor as prediction parameters. Then, we used the deep neural network algorithm for the prediction. Furtherly, we made an optimization for the hyper-parameters of the deep learning algorithm and obtained improved results than the previous IonSeq method.

Spontaneous Lithiation of Binary Oxides during Epitaxial Growth on LiCoO2.

Epitaxial growth is a powerful tool for synthesizing heterostructures and integrating multiple functionalities. However, interfacial mixing can readily occur and significantly modify the properties of layered structures, particularly for those containing energy storage materials with smaller cations. Here, we show a two-step sequence involving the growth of an epitaxial LiCoO2 cathode layer followed by the deposition of a binary transition metal oxide. Orientation-controlled epitaxial synthesis of the model solid-state-electrolyte Li2WO4 and anode material Li4Ti5O12 occurs as WO3 and TiO2 nucleate and react with Li ions from the underlying cathode. We demonstrate that this lithiation-assisted epitaxy approach can be used for energy materials discovery and exploring different combinations of epitaxial interfaces that can serve as well-defined model systems for mechanistic studies of energy storage and conversion processes.

Identification and Validation of a GPX4-Related Immune Prognostic Signature for Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is a commonly occurring histological subtype of lung cancer. Glutathione peroxidase 4 (GPX4) is an important regulatory factor of ferroptosis and is involved in the development of many cancers, but its prognostic significance has not been systematically described in LUAD. In this study, we focused on developing a robust GPX4-related prognostic signature (GPS) for LUAD. Data for the training cohort was extracted from The Cancer Genome Atlas, and that for the validation cohort was sourced from the GSE72094 dataset including 863 LUAD patients. GPX4-related genes were screened out by weighted gene coexpression network analysis and Spearman's correlation analysis. Then, Cox regression and least absolute shrinkage and selection operator regression analyses were employed to construct a GPS. The ESTIMATE algorithm, single-sample gene set enrichment analysis (ssGSEA), and GSEA were utilized to evaluate the relationship between GPS and the tumor microenvironment (TME). We constructed and validated a GPS premised on four GPX4-related genes (KIF14, LATS2, PRKCE, and TM6SF1), which could classify LUAD patients into low- and high-score cohorts. The high-risk cohort presented noticeably poorer overall survival (OS) as opposed to the low-risk cohort, meaning that the GPS may be utilized as an independent predictor of the OS of LUAD. The GPS was also adversely correlated with multiple tumor-infiltrating immune cells and immune-related processes and pathways in TME. Furthermore, greater sensitivity to erlotinib and lapatinib were identified in the low-risk cohort based on the GDSC database. Our findings suggest that the GPS can effectively forecast the prognosis of LUAD patients and may possibly regulate the TME of LUAD.

Recognizing protein-metal ion ligands binding residues by random forest algorithm with adding orthogonal properties.

Accurately identifying protein-metal ion ligand binding residues is the key to study protein functions. Because the number of binding residues and non-binding residues is significantly imbalanced, false positives is hard to be eliminated from the binding residues prediction result. Therefore, identification of protein-metal ion ligand binding residues remains challenging. In this paper, the binding site of 7 metal ions (Ca2+, Mg2+, Zn2+, Fe3+, Mn2+, Cu2+ and Co2+) were used as the objects of the study. Besides generally adopted parameters: amino acids and predicted secondary structure information, we creatively introduced ten orthogonal properties as a parameter. These orthogonal properties are clustering of 188 physical and chemical characteristics that can be used to describe three-dimension structural information. With the optimized parameters, we used the Random Forest algorithm to predict ion ligand binding residues. The proposed method obtained good prediction results with the MCC values of Mg2+, Ca2+ and Zn2+ reaching 0.255, 0.254, 0.540, respectively. Comparing to the IonSeq method, the method developed in this paper has advantages on the binding residues prediction of some ions.